海洋模拟的概率进化方法：从Lorenz-63到理想化海洋模型
On a probabilistic evolutionary approach to ocean modelling: From Lorenz-63 to idealized ocean models
摘要：
在这项研究中，我们开发了一种替代方法来模拟海洋，反映海洋流动的混沌性质和海洋模型的不确定性-而不是利用经典的确定性或随机微分方程，我们提供了一种概率进化方法（PEA），利用相空间中的概率动力学。在这项工作中提出的数据驱动版本的PEA的主要特点是，它不需要知道背后的流动动力学的物理模型it. Within PEA框架，我们开发了两个概率进化方法，这是基于概率进化模型，使用准时不变结构的相空间。该方法已被测试的Lorenz 63系统和理想化的多层准地转模式所产生的完整和不完整的参考数据集。结果表明，这两种方法再现大规模和小规模的参考流的功能，通过保持参考相空间（参考流的相空间）内的概率动力学。所提出的方法提供了有吸引力的好处和一个很大的灵活性，海洋建模工作的数学模型和测量。最显著的一点是，它提供了一个替代主流的海洋参数化，不需要修改现有的海洋模式，并易于实现。此外，它不依赖于输入数据的性质，因此可以与数值计算的流量和来自不同来源（漂移，气象站等）的实际测量一起工作。
英文摘要：
In this study we develop an alternative way to model the ocean reflecting the chaotic nature of ocean flows and uncertainty of ocean models -- instead of making use of classical deterministic or stochastic differential equations we offer a probabilistic evolutionary approach (PEA) that capitalizes on the use of probabilistic dynamics in phase space. The main feature of the data-driven version of PEA proposed in this work is that it does not require to know the physics behind the flow dynamics to model it. Within the PEA framework we develop two probabilistic evolutionary methods, which are based on probabilistic evolutionary models using quasi time-invariant structures in phase space. The methods have been tested on complete and incomplete reference data sets generated by the Lorenz 63 system and by an idealized multi-layer quasi-geostrophic model. The results show that both methods reproduce large- and small-scale features of the reference flow by keeping the probabilistic dynamics within the reference phase space (the phase space of the reference flow). The proposed approach offers appealing benefits and a great flexibility to ocean modellers working with mathematical models and measurements. The most remarkable one is that it provides an alternative to the mainstream ocean parameterizations, requires no modification of existing ocean models, and easy to implement. Moreover, it does not depend on the nature of input data, and therefore can work with both numerically-computed flows and real measurements from different sources (drifters, weather stations, etc.).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
